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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Roudbari, Asal Dalvand, Mahdi Pasiar, Navid Asadi, Navid Yousefi, Mohammad Masouleh, Mehdi Tale Sharif University of Technology Electrical Engineering Department Tehran Iran University of Tehran Human Robot Interaction Lab Tehran Iran University of Tehran Human Robot Interaction Lab School of Electrical Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Autonomous robotic grasping has emerged as a valuable capability for automating everyday household tasks like table setting (bossing), cleaning, and meal preparation. Still, accurately detecting and manipulating diverse everyday objects in unstructured home environments remains a significant challenge. In this study, an integrated robotic system combining YOLOv8s-seg deep learning for object detection and segmentation with computer vision techniques to determine optimal grasping points is proposed. The accuracy of this model has also been compared with other models through their mAP0.5, mAP.05-.95, precision and recall. A custom dataset of over 1000 images containing common household objects was created, with manual annotation and augmentation to train the YOLOv8s-seg segmentation model. The approach processes input images to understand desired arrangements and identify target objects and their locations for grasping. The detected objects are localized using camera-to-robot calibration and aligned with estimated orientations from computer vision techniques. Extensive tests have proven that representative household objects in cluttered arrangements have a grasping success rate of over 90% using a 3DOF Delta Parallel robot with a 2-finger gripper guided entirely by visual perception. By overcoming real-world challenges like transparent and reflective materials, the perception-guided robots provide precise and efficient robotic grasping in unstructured home environments. This integrated approach combines robust perception and localization, providing promising evidence for reliable automation of repetitive household organization tasks1.1A video of real-time grasp performance is uploaded as supplementary material. © 2023 IEEE.

关键词： Object detection

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Demand Forecasting in Transportation: A Graph Attention Networks for Predicting Bus Ridership 27

Demand Forecasting in Transportation: A Graph Attention Netw...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Munkhbayar, Tsetsentsengel Dashdorj, Zolzaya Jargalsaikhan, Zoljargal Tae-Koo, Kang Altangerel, Erdenebaatar Department of Computer Science Mongolian University of Science and Technology Ulaanbaatar Mongolia Department of Human Intelligence and Robot Engineering Sangmyung University Seoul Korea Republic of

ISBN: (纸本)9791188428137

Demand prediction in transportation systems plays a critical role in optimizing resources and improving service efficiency. This study explores demand prediction for Ulaanbaatar's public transportation network using Graph Attention Networks (GATs), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). GATs effectively capture spatial relationships, achieving the best performance while GANs struggle with stability and convergence issues. The findings emphasize the potential of using graph-based methods that incorporate key stations in the analysis of public transportation networks for predicting transit demand. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Generative adversarial networks

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Theta synchronization as a neural marker of flexible (re-)use of socio-cognitive mechanisms for a new category of (artificial) interaction partners

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Cortex 2023年 169卷 249-258页

作者： Parenti, Lorenzo Navare, Uma Prashant Marchesi, Serena Roselli, Cecilia Wykowska, Agnieszka Social Cognition in Human-Robot Interaction (S4HRI) Italian Institute of Technology Genova Italy Department of Psychology University of Turin Turin Italy Department of Computer Science University of Manchester Manchester United Kingdom

Previous work shows that in some instances artificial agents, such as robots, can elicit higher-order socio-cognitive mechanisms, similar to those elicited by humans. This suggests that these socio-cognitive mechanisms, such as mentalizing processes, originally developed for interaction with other humans, might be flexibly (re-)used, or “hijacked”, for approaching this new category of interaction partners (Wykowska, 2020). In this study, we set out to identify neural markers of such flexible reuse of socio-cognitive mechanisms. We focused on fronto-parietal theta synchronization, as it has been proposed to be a substrate of cognitive flexibility in general (Fries, 2005). We analyzed EEG data from two experiments (Bossi et al., 2020;Roselli et al., submitted), in which participants completed a test measuring their individual likelihood to adopt the intentional stance towards robots, the intentional stance (IST) test. Our results show that participants with higher scores on the IST, indicating that they had higher likelihood of adopting the intentional stance towards a robot, had a significantly higher theta synchronization value, relative to participants with lower scores on the IST. These results suggest that long-range synchronization in the theta band might be a marker socio-cognitive process that can be flexibly applied towards non-human agents, such as robots. © 2023 The Author(s)

关键词： Connectivity EEG human-robot interaction Intentional stance

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CLUE HOG: An Immersive Competitive Lock-Unlock Experience using Hook On Go-Go Technique for Authentication in the Metaverse

CLUE HOG: An Immersive Competitive Lock-Unlock Experience us...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Alexander Giovannelli Francielly Rodrigues Shakiba Davari Ibrahim A. Tahmid Logan Lane Cherelle Connor Kylie Davidson Gabriella N. Ramirez Brendan David-John Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Blacksburg VA USA

This paper presents our solution to the 2023 3DUI Contest challenge. Our goal was to provide an immersive VR experience to engage users in privately securing and accessing information in the Metaverse while improving authentication-related interactions inside our virtual environment. To achieve this goal, we developed an authentication method that uses a virtual environment's individual assets as security tokens. To improve the token selection process, we introduce the HOG interaction technique. HOG combines two classic interaction techniques, Hook and Go-Go, and improves approximate object targeting and further obfuscation of user password token selections. We created an engaging mystery-solving mini-game to demonstrate our authentication method and interaction technique.

关键词： Three-dimensional displays Metaverse Conferences Authentication Passwords User interfaces Observers

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition 8

SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera...

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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Siahkali, Farbod Alavi, Seyed Amirmahdi Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Islamic Azad University Human and Robot Interaction Laboratory School of Computer Science Central Tehran Branch Tehran Iran

ISBN: (纸本)9781665476232

In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks.1https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition © 2022 IEEE.

关键词： Model automobiles

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A Communication-Focused Framework for Understanding Immersive Collaboration Experiences

A Communication-Focused Framework for Understanding Immersiv...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Jerald Thomas Sang Won Lee Alexander Giovannelli Logan Lane Doug Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Blacksburg VA USA

The ability to collaborate with other people across barriers created by time and/or space is one of the greatest features of modern communication. Immersive technologies are positioned to enhance this ability to collaborate even further. However, we do not have a firm understanding of how specific immersive technologies, or components thereof, alter the ability for two or more people to communicate, and hence collaborate. In this work-in-progress position paper, we propose a new framework for immersive collaboration experiences and provide an example of how it could be used to understand a hybrid collaboration among two co-located users and one remote user. We are seeking feedback from the community before conducting a formal evaluation of the framework. We also present some future work that this framework could facilitate.

关键词： Social computing Solid modeling Three-dimensional displays Conferences Computational modeling Collaboration Virtual reality

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Toward Anxiety-Reducing Pocket Robots for Children

Toward Anxiety-Reducing Pocket Robots for Children

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International Conference on Ubiquitous Robots (UR)

作者： Morten Roed Frederiksen Kasper Stoy Maja Matarić Computer Science Department Interaction Lab University of Southern California Copenhagen S Computer Science Department Interaction Lab University of Southern California

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

A common denominator for most therapy treatments for children who suffer from an anxiety disorder is daily practice routines to learn techniques needed to overcome anxiety. However, applying those techniques while experiencing anxiety can be highly challenging. This paper presents the design, implementation, and pilot study of a tactile hand-held pocket robot “AffectaPocket”, designed to work alongside therapy as a focus object to facilitate coping during an anxiety attack. The robot does not require daily practice to be used, has a small form factor, and has been designed for children 7 to 12 years old. The pocket robot works by sensing when it is being held and attempts to shift the child's focus by presenting them with a simple three-note rhythm-matching game. We conducted a pilot study of the pocket robot involving four children aged 7 to 10 years, and then a main study with 18 children aged 6 to 8 years; neither study involved children with anxiety. Both studies aimed to assess the reliability of the robot's sensor configuration, its design, and the effectiveness of the user tutorial. The results indicate that the morphology and sensor setup performed adequately and the tutorial process enabled the children to use the robot with little practice. This work demonstrates that the presented pocket robot could represent a step toward developing low-cost accessible technologies to help children suffering from anxiety disorders.

关键词： Anxiety disorders Sociology Medical treatment Tactile sensors Tutorials Games Aging

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CoLT: Enhancing Collaborative Literature Review Tasks with Synchronous and Asynchronous Awareness Across the Reality-Virtuality Continuum

CoLT: Enhancing Collaborative Literature Review Tasks with S...

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Ibrahim A. Tahmid Francielly Rodrigues Alexander Giovannelli Lee Lisle Jerald Thomas Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Blacksburg VA USA

Collaboration plays a vital role in both academia and industry whenever we need to browse through a big amount of data to extract meaningful insights. These collaborations often involve people living far from each other, with different levels of access to technology. Effective cross-border collaborations require reliable telepresence systems that provide support for communication, cooperation, and understanding of contextual cues. In the context of collaborative academic writing, while immersive technologies offer novel ways to enhance collaboration and enable efficient information exchange in a shared workspace, traditional devices such as laptops still offer better readability for longer articles. We propose the design of a hybrid cross-reality cross-device networked system that allows the users to harness the advantages of both worlds. Our system allows users to import documents from their personal computers (PC) to an immersive headset, facilitating document sharing and simultaneous collaboration with both colocated colleagues and remote colleagues. Our system also enables a user to seamlessly transition between Virtual Reality, Augmented Reality, and the traditional PC environment, all within a shared workspace. We present the real-world scenario of a global academic team conducting a comprehensive literature review, demonstrating its potential for enhancing cross-reality hybrid collaboration and productivity.

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Situational Adaptive Autonomy in human-AI Cooperation

Situational Adaptive Autonomy in Human-AI Cooperation

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2023 Workshop on Intervening, Teaming, Delegating, AutomationXP 2023

作者： Salikutluk, Vildan Frodl, Eric Herbert, Franziska Balfanz, Dirk Koert, Dorothea Interactive AI Algorithms & Cognitive Models for Human-AI Interaction Computer Science Department Technical University Darmstadt Germany Models of Higher Cognition Human Sciences Department Technical University Darmstadt Germany Centre for Cognitive Science Technical University Darmstadt Germany

human-AI teams have the potential to produce improved outcomes in various tasks as opposed to each team member working alone. However, there are various factors that influence human-AI team performance which potentially differ from classical human-computer interaction settings. Specifically, there is existing work indicating that it is beneficial for AI systems to automatically adapt their autonomy within the team and task in order to work towards achieving a shared goal more effectively. Thus, in this paper, we describe a concept of situational adaptive autonomy for human-AI cooperation in a shared workspace setting. We discuss that task complexity and models for the AI system’s understanding of their human teammate, i.e. Theory of Mind models (ToMMs) might be helpful to implement situational adaptation of AI autonomy such that interaction and team performance can be improved. We present an experimental setup for a cooperative real-world robotic task and a corresponding approximation in a grid-world in which we plan to investigate situational adaptive autonomy within a shared workspace in an interactive human-AI team. © 2023 Copyright for this paper by its authors.

关键词： human computer interaction

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks 12

Real-Time Facial Expression Recognition using Facial Landmar...

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12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

作者： Haghpanah, Mohammad A. Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Lab School of Electrical and Computer Engineering Tehran Iran University of Nevada Department of Computer Science and Engineering Reno United States

ISBN: (纸本)9781665412162

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. © 2022 IEEE.

关键词： Emotion Recognition

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